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Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network
CMS Collaboration
2013
发表期刊PHYSICAL REVIEW D
卷号87期号:7
通讯作者Chatrchyan, S (reprint author), Yerevan Phys Inst, Yerevan 375036, Armenia.
摘要In this paper, a search for supersymmetry (SUSY) is presented in events with two opposite-sign isolated leptons in the final state, accompanied by hadronic jets and missing transverse energy. An artificial neural network is employed to discriminate possible SUSY signals from a standard model background. The analysis uses a data sample collected with the CMS detector during the 2011 LHC run, corresponding to an integrated luminosity of 4: 98 fb(-1) of proton-proton collisions at the center-of-mass energy of 7 TeV. Compared to other CMS analyses, this one uses relaxed criteria on missing transverse energy (E-T > 40 GeV) and total hadronic transverse energy (HT > 120 GeV), thus probing different regions of parameter space. Agreement is found between standard model expectation and observations, yielding limits in the context of the constrained minimal supersymmetric standard model and on a set of simplified models. DOI: 10.1103/PhysRevD.87.072001
关键词STANDARD MODEL PP COLLISIONS ROOT-S=7 TEV PHYSICS JETS
学科领域Astronomy & Astrophysics; Physics
DOI10.1103/PhysRevD.87.072001
收录类别SCI
语种英语
WOS类目Astronomy & Astrophysics ; Physics, Particles & Fields
WOS记录号WOS:000316954200001
引用统计
被引频次:8[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ihep.ac.cn/handle/311005/213497
专题粒子天体物理中心
作者单位中国科学院高能物理研究所
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CMS Collaboration. Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network[J]. PHYSICAL REVIEW D,2013,87(7).
APA CMS Collaboration.(2013).Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network.PHYSICAL REVIEW D,87(7).
MLA CMS Collaboration."Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network".PHYSICAL REVIEW D 87.7(2013).
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